Next generations of wireless communication systems are likely to include data rates in the order of several Mbps using digital modulation formats having good spectral efficiency and high tolerance to external interferences, such as jamming in military applications or RF signals from other communication systems or other members of the same wireless network. In addition, modulation formats having small dynamic range of transmitted signal power may be preferred since they do not require linear amplification over a large dynamic range and thus enable using low-cost power-efficient amplifiers.
These requirements can be satisfied in communication systems using spread spectrum (SS) transmission techniques and continuous phase modulation (CPM). The CPM waveform is characterized in that the transmitted information is contained in the phase of the transmitted electromagnetic signal which changes continuously from symbol to symbol, while the signal envelope remains substantially constant. With BPSK (binary phase shift keying) a logic one is transmitted as one phase of a modulated signal and a logic zero is transmitted as a 180-degree shifted phase with a sharp transition in phase. This sharp phase transition results in broadening of the transmitted spectrum. With CPM the phase of the transmitted signal makes smooth phase changes over the symbol transitions of the modulating digital signal. A particular form of CPM is minimum-shift keying (MSK), in which the phases that the modulated signal is permitted to take at a given symbol time are only the phases adjacent to the previous symbol phase. M-ary CPM formats are possible wherein each CPM symbol interval contains mb=log2(M) bits of information.
Receivers of wireless CPM signals are generally divided according to the phase detection methods used therein into coherent and non-coherent receivers. To accomplish coherent detection, the phase of the received signal has to be continuously recovered and estimated prior to de-modulation and detection of the information symbols. Non-coherent receivers require only a phase difference of the received signal to be analyzed over a relatively short time interval of at least two symbol durations and are designed to average the phase of the received signal thus substantially ignoring the effects of phase error in the de-modulation and detection of the information symbols.
In a typical communication channel the wireless signal experiences signal deterioration, e.g. due to the presence of noise, which introduces phase errors and loss of the data when the signal is demodulated at a receiver in the communications link. These errors can be at least partially recovered using channel encoding at the transmitter, thereby enabling forward error correction, and channel decoding at the receiver. Traditionally, coherent receivers have been known to enable better error correction performance than non-coherent receivers, due to the additional phase information that such receivers can use at the decoding stage. However, such detectors require complicated arrangements for phase recovery and complicated signal processing techniques, where sometimes the number of signal states that have to be analyzed are quite large.
The coherent detection becomes even more problematic if the CPM format is combined with a spread spectrum technique of frequency hopping (FH), e.g. to reduce external interference such as jamming. In this context, a high data rate CPM transmission is attainable for so-called slow FH, when multiple symbols are transmitted during one hopping period. One problem with using CPM in slow FH, however, is the phase discontinuity of the CPM symbols between adjacent hop intervals that precludes many conventional methods of phase recovery. Coherent detection of the FH CPM signals is nevertheless possible by correcting the phase discontinuity via suitable choice of a preamble signal prior to the detection. However, this requires a dedicated preamble signal embedded in the transmission waveform during each hop interval, which reduces the spectral efficiency of the transmission scheme.
Therefore, it would be advantageous to use a non-coherent detection technique to detect and decode CPM signals, and a number of such techniques have been disclosed in the art. However, many conventional non-coherent CPM detectors either suffer from a considerable, as high as 3 dB, penalty in signal to noise ratio (SNR) compared to the optimal coherent detectors, or require excessively complex processing. For example, U.S. Pat. No. 5,017,883 in the names of Divsalar and Simon teaches a multiple symbol differential detection technique (MSDD) which uses a multiple symbol observation interval on the basis of which a joint decision is made regarding the phase of the received symbols. This method is capable of providing a good SNR for long observation intervals, but at the expense of examining a very large number of possible symbol sequences to make optimal decisions.
The low SNR performance of transmission systems using CPM formats can be improved using channel coding. Block codes and convolutional codes are two types of channel codes commonly used in the art of channel coding. A block code is an error detection and/or correction code in which an encoded block of data consists of n encoded bits, containing k information bits (k<n) and n-k redundant check bits to detect and/or correct most errors. Types of block codes known in the art include Hamming codes, Golay code, B CH codes, and Reed Solomon codes.
Convolutional codes are widely used in the communications art to provide error correction. Convolutional codes continuously convert an entire data stream to encode the k information bits. The encoded bit stream depends on the current information bits and also on the previous input information bits. With a convolutional code, k information bits are encoded into n encoded bits in an encoder with m memory stages that store the state information of the encoder. A constraint length K of a convolutional code is defined as m+1 and a code rate r as k/n. The well-known Viterbi algorithm is commonly used to decode convolutional codes.
Known decoding approaches can be divided in two categories in accordance with how they utilize an incoming analogue information stream: these are a hard-decision decoding and a soft decision decoding. Hard-decision decoders start with input information in a digitized form of code symbols, or “hard decisions”, and use decoding algorithms to attempt to correct any errors that have occurred. Soft-decision decoding (SDD) on the other hand utilizes additional information present in the received data stream. SDD starts with soft decision data that may include hard information indicating which value each received symbol is assigned (e.g. a “1” or a “0” for binary symbols) and an associated value that indicates a reliability or confidence that the value assigned to a particular received symbol is correct. This is generally referred to as “soft input” information. A decoder then utilizes the soft input information to decode the received information so as to produce a code word most likely to represent the original transmitted data.
Most of decoding methods for soft-in, soft-out (SISO) decoding are approximate implementations of an a-posteriori probability (APP) decoder, also referred to as the maximum a posteriori (MAP) decoder. An APP decoder finds a probability of each data symbol at each symbol time given the entire received signal. This is in contrast to the well-known Viterbi algorithm, which finds the entire sequence that was most likely transmitted given the received signal. Both algorithms are optimum for their respective criteria, but the APP decoding scheme more naturally provides the soft output information. Log-APP is a form of APP processing where the quantities manipulated are not probabilities, but rather “log-probability quantities” derived from probabilities. The term “log-probability quantity,” herein refers to log-probabilities, log-probabilities with offsets, sums of log-probabilities, differences of log-probabilities, and combinations of these. Note that a “log-probability” is simply a logarithm of a probability; the base of the logarithm is arbitrary. Manipulating log-probability quantities, rather than working with the probabilities themselves, is generally preferred due to computational issues such as a finite-precision representation of numbers.
Recently, an efficient coding technique, called turbo coding, requiring SISO decoding have been developed, enabling data transmission performance near the theoretical limit. A turbo code is generated at a transmitter by a serial or parallel concatenation of two or more component codes, often recursive convolutional codes, each separated by an interleaver. Turbo decoding at a receiver uses a soft decoder at the input followed by an inverse interleaver and a second soft decoder. The output of the second soft decoder feeds back to the input of the first soft decoder through an interleaver. The data is passed through the turbo decoder in several iterations with each pass improving the quality of error correction.
Serially concatenated codes that use the CPM as the inner recursive code have been shown to offer good error correction performance when coupled with turbo-like SISO decoders based on the APP. The BEAM modem described in U.S. Pat. No. 6,968,021 provides an example of turbo-like decoder for such a serially-encoded CPM signal, wherein an inner coherent CPM decoder and an outer trellis-based decoder cooperate to iteratively improve the error correction. However, the BEAM receiver requires a computationally complex coherent inner CPM decoder and an involved phase recovery mechanism for achieving the coherent detection.
In another example of prior-art iterative CPM decoding, a paper by H. Kim, Q. Zhao, G. L. Stuber, and K. R. Narayanan, entitled “Anti-jamming Performance of Slow FH-CPM Signals with Concatenated Coding and Jamming Estimation”, in IEEE Military Communications Conference, Oct. 16-18, 2003, teaches non-coherent CPM detection in application to frequency hopping in a tactical environment. In this article, an iterative MAP based approach to detecting the CPM signal over one hop duration is presented. The inner CPM decoder taught in this article is based on the MSDD technique, which may require significant computational recourses.
An object of this invention is to provide an efficient iterative non-coherent detector for encoded CPM signals.
Another object of this invention is to provide a low complexity method of iterative decoding of serially encoded CPM signals.
Another object of this invention is to provide a low-complexity iterative non-coherent detector for frequency-hopping encoded CPM signals.